Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Investigation of long short-term memory artificial neural networks as estimators of nitrate concentrations in soil from measured electrical impedance spectra

Title data

Ma, Xiaohu ; Fischerauer, Gerhard:
Investigation of long short-term memory artificial neural networks as estimators of nitrate concentrations in soil from measured electrical impedance spectra.
In: Kanoun, Olfa (ed.): Abstract Book 15th International Workshop on Impedance Spectroscopy (IWIS 2022). - Chemnitz , 2022 . - pp. 125-128
ISBN 978-3-949744-01-3

Abstract in another language

Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents an investigation of long short-term memory (LSTM) recurrent artificial neural networks with regard to their suitability to extract nitrate concentrations from electrical impedance spectra of soil samples. Based on measured impedance spectra and physical properties of various synthetic sandy soils, the importance of different features for model training was investigated first. Both Random Forests and LSTM were tested as feature selection methods. Then numerous LSTM networks were trained to predict the nitrate concentration in sandy soils. The resulting regression models showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95.

Further data

Item Type: Article in a book
Refereed: No
Keywords: Electrical impedance spectroscopy; EIS; long short-term memory; LSTM; recurrent neural network; RNN; feature selection; nitrate
Institutions of the University: Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology
Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer
Faculties
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 12 Oct 2022 12:45
Last Modified: 12 Oct 2022 12:45
URI: https://eref.uni-bayreuth.de/id/eprint/71743